def test_sentinelize(self):
stream = fstream([(10, 12, 0.5), (14, 15, 1.2)],
fields=['start', 'end', 'score'])
stream = sentinelize(stream, 'Z')
for y in stream:
x = y
self.assertEqual(x, 'Z')
def _stream(ts, tf):
X = [common.sentinelize(x, [sys.maxint] * len(x.fields)) for x in ts]
S = [[(-sys.maxint, -sys.maxint, 0.0)] for t in ts]
start_idx = tf.fields.index('start')
end_idx = tf.fields.index('end')
if hasattr(method, '__call__'):
mean_fn = lambda scores, denom: method(scores)
else:
mean_fn = _score_functions.get(method, _arithmetic_mean)
for y in tf:
ystart = y[start_idx]
yend = y[end_idx]
scores = ()
for i in range(len(ts)):
xnext = S[i][-1]
# Load into S all score items which intersect feature y
while xnext[0] < yend:
xnext = X[i].next()
if xnext[1] > ystart: S[i].append(xnext)
n = 0
while S[i][n][1] <= ystart:
n += 1
S[i] = S[i][n:]
scores_y = []
for s in S[i]:
if yend <= s[0]: continue
if s[0] < ystart: start = ystart
else: start = s[0]
if yend < s[1]: end = yend
else: end = s[1]
scores_y.extend([s[2]] * (end - start))
scores += (mean_fn(scores_y, 1.0 / (yend - ystart)), )
yield tuple(y) + scores
def merge_scores(trackList, method="arithmetic"):
"""
Creates a stream with per-base average of several score tracks::
X1: __________666666666______
X2: _____2222222222__________
R: _____11111444443333______
:param trackList: list of FeatureStream objects.
:param method: (str) type of average: one of 'arithmetic','geometric', or 'sum' (no average).
:rtype: FeatureStream
"""
tracks = [FeatureStream(common.sentinelize(x, [sys.maxint] * len(x.fields)), x.fields) for x in trackList]
tracks = [common.reorder(t, ["start", "end", "score"]) for t in tracks]
fields = [f for f in tracks[0].fields if all([f in t.fields for t in tracks])] # common fields
elements = [list(x.next()) for x in tracks]
track_denom = 1.0 / len(trackList)
if hasattr(method, "__call__"):
mean_fn = lambda scores, denom: method(scores)
else:
mean_fn = _score_functions.get(method, _arithmetic_mean)
for i in xrange(len(tracks) - 1, -1, -1):
if elements[i][0] == sys.maxint:
tracks.pop(i)
elements.pop(i)
def _stream(tracks):
while tracks:
start = min([x[0] for x in elements])
end = min([x[0] for x in elements if x[0] > start] + [x[1] for x in elements])
scores = [x[2] for x in elements if x[1] > start and x[0] < end]
if len(fields) > 3:
rest = []
for i in range(len(fields[3:])):
r = [str(x[3 + i]) for x in elements if not (x[3 + i] is None) and x[1] > start and x[0] < end]
if all([x == r[0] for x in r]):
rest.append(r[0])
else:
rest.append("|".join(r))
yield (start, end, mean_fn(scores, track_denom)) + tuple(rest)
else:
yield (start, end, mean_fn(scores, track_denom))
for i in xrange(len(tracks) - 1, -1, -1):
if elements[i][0] < end:
elements[i][0] = end
if elements[i][1] <= end:
elements[i] = list(tracks[i].next())
if elements[i][0] == sys.maxint:
tracks.pop(i)
elements.pop(i)
return FeatureStream(_stream(tracks), fields)
def _stream(ts, tf):
tf = common.sentinelize(tf, [sys.maxint] * len(tf.fields))
info_idx = [k for k, f in enumerate(tf.fields) if f not in ts.fields]
if stranded:
ts_strand_idx = ts.fields.index("strand")
tf_strand_idx = tf.fields.index("strand")
same_strand = lambda x, y: x[ts_strand_idx] == y[tf_strand_idx]
else:
same_strand = lambda x, y: True
Y = []
ynext = (-sys.maxint, -sys.maxint, 0.0)
for x in ts:
xstart = x[0]
xend = x[1]
# Load into Y all feature items which intersect score x
while ynext[0] < xend:
if ynext[1] > xstart:
Y.append(ynext)
ynext = tf.next()
# Remove features that are far behind x
if Y:
n = 0
try:
while Y[n][1] <= xstart:
n += 1
Y = Y[n:]
except IndexError:
Y = [ynext]
# Yield intersections
for y in Y:
if not same_strand(x, y):
continue
info = tuple([y[k] for k in info_idx]) if annotate else ()
if strict and (y[0] > xstart or y[1] < xend):
continue
if y[0] >= xend:
continue # keep for next iteration
start = xstart if y[0] < xstart else y[0]
end = xend if y[1] > xend else y[1]
yield (start, end) + tuple(x[2:]) + info
def _stream(ts, tf):
X = [common.sentinelize(x, [sys.maxint] * len(x.fields)) for x in ts]
S = [[(-sys.maxint, -sys.maxint, 0.0)] for t in ts]
start_idx = tf.fields.index("start")
end_idx = tf.fields.index("end")
if hasattr(method, "__call__"):
mean_fn = lambda scores, denom: method(scores)
else:
mean_fn = _score_functions.get(method, _arithmetic_mean)
for y in tf:
ystart = y[start_idx]
yend = y[end_idx]
scores = ()
for i in range(len(ts)):
xnext = S[i][-1]
# Load into S all score items which intersect feature y
while xnext[0] < yend:
xnext = X[i].next()
if xnext[1] > ystart:
S[i].append(xnext)
n = 0
while S[i][n][1] <= ystart:
n += 1
S[i] = S[i][n:]
scores_y = []
for s in S[i]:
if yend <= s[0]:
continue
if s[0] < ystart:
start = ystart
else:
start = s[0]
if yend < s[1]:
end = yend
else:
end = s[1]
scores_y.extend([s[2]] * (end - start))
scores += (mean_fn(scores_y, 1.0 / (yend - ystart)),)
yield tuple(y) + scores
def _stream(ts, tf):
tf = common.sentinelize(tf, [sys.maxint] * len(tf.fields))
info_idx = [k for k, f in enumerate(tf.fields) if f not in ts.fields]
if stranded:
ts_strand_idx = ts.fields.index('strand')
tf_strand_idx = tf.fields.index('strand')
same_strand = lambda x, y: x[ts_strand_idx] == y[tf_strand_idx]
else:
same_strand = lambda x, y: True
Y = []
ynext = (-sys.maxint, -sys.maxint, 0.0)
for x in ts:
xstart = x[0]
xend = x[1]
# Load into Y all feature items which intersect score x
while ynext[0] < xend:
if ynext[1] > xstart:
Y.append(ynext)
ynext = tf.next()
# Remove features that are far behind x
if Y:
n = 0
try:
while Y[n][1] <= xstart:
n += 1
Y = Y[n:]
except IndexError:
Y = [ynext]
# Yield intersections
for y in Y:
if not same_strand(x, y): continue
info = tuple([y[k] for k in info_idx]) if annotate else ()
if strict and (y[0] > xstart or y[1] < xend): continue
if y[0] >= xend: continue # keep for next iteration
start = xstart if y[0] < xstart else y[0]
end = xend if y[1] > xend else y[1]
yield (start, end) + tuple(x[2:]) + info
def merge_scores(trackList, method='arithmetic'):
"""
Creates a stream with per-base average of several score tracks::
X1: __________666666666______
X2: _____2222222222__________
R: _____11111444443333______
:param trackList: list of FeatureStream objects.
:param method: (str) type of average: one of 'arithmetic','geometric', or 'sum' (no average).
:rtype: FeatureStream
"""
tracks = [
FeatureStream(common.sentinelize(x, [sys.maxint] * len(x.fields)),
x.fields) for x in trackList
]
tracks = [common.reorder(t, ['start', 'end', 'score']) for t in tracks]
fields = [
f for f in tracks[0].fields if all([f in t.fields for t in tracks])
] # common fields
elements = [list(x.next()) for x in tracks]
track_denom = 1.0 / len(trackList)
if hasattr(method, '__call__'):
mean_fn = lambda scores, denom: method(scores)
else:
mean_fn = _score_functions.get(method, _arithmetic_mean)
for i in xrange(len(tracks) - 1, -1, -1):
if elements[i][0] == sys.maxint:
tracks.pop(i)
elements.pop(i)
def _stream(tracks):
while tracks:
start = min([x[0] for x in elements])
end = min([x[0] for x in elements if x[0] > start] +
[x[1] for x in elements])
scores = [x[2] for x in elements if x[1] > start and x[0] < end]
if len(fields) > 3:
rest = []
for i in range(len(fields[3:])):
r = [
str(x[3 + i]) for x in elements if
not (x[3 + i] is None) and x[1] > start and x[0] < end
]
if all([x == r[0] for x in r]):
rest.append(r[0])
else:
rest.append("|".join(r))
yield (start, end, mean_fn(scores, track_denom)) + tuple(rest)
else:
yield (start, end, mean_fn(scores, track_denom))
for i in xrange(len(tracks) - 1, -1, -1):
if elements[i][0] < end:
elements[i][0] = end
if elements[i][1] <= end:
elements[i] = list(tracks[i].next())
if elements[i][0] == sys.maxint:
tracks.pop(i)
elements.pop(i)
return FeatureStream(_stream(tracks), fields)
def concatenate(trackList, fields=None, remove_duplicates=False, group_by=None, aggregate={}):
"""
Returns one stream containing all features from a list of tracks, ordered by *fields*.
:param trackList: list of FeatureStream objects.
:param fields: (list of str) list of fields to keep in the output (at least ['start','end']).
:param remove_duplicates: (bool) whether to remove items that are identical in several
of the tracks in *trackList*. [False]
:param group_by: (list of str) if specified, elements having all values for these fields in
common will be merged into a singe element. Other fields are merged according to *aggregate*
if specified, or `common.generic_merge` by default.
:aggregate: (dict) for each field name given as a key, its value is the function
to apply to the vector containing all different values for this field in order to merge them.
E.g. ``{'score': lambda x: sum(x)}`` will return the sum of all scores in the output.
:rtype: FeatureStream
"""
def _find_min(feat_tuple):
"""Return the index of the 'smallest' element amongst a tuple of features from
different tracks. Priority is given to the first field; if the first field items
are equal amongst several elements, it looks at the second field, a.s.o."""
nmin = 0
xmin = feat_tuple[0]
for n,x in enumerate(feat_tuple[1:]):
if x[0] == sys.maxint: continue
for k in range(len(x)):
if cmp(hash(x[k]),hash(xmin[k]))<0:
xmin = x
nmin = n+1
break
elif cmp(hash(x[k]),hash(xmin[k]))>0:
break
return nmin
def _weave(_t,N):
"""Generator yielding all features represented in a list of tracks *_t*,
sorted w.r.t the *N* first fields."""
current = [x.next()[:N] for x in _t] # init
allfields = [t.fields for t in _t]
n = _find_min(current)
last = current[n]
current[n] = _t[n].next()[:N]
if not group_by: yield last
while 1:
# Remove duplicates
if remove_duplicates:
while not all([current.count(x)==1 for x in current]):
for k in range(len(current)):
if current.count(current[k]) > 1:
current[k] = _t[k].next()[:N]
n = _find_min(current)
if current[n][0] == sys.maxint: break
if group_by:
idx = [allfields[n].index(f) for f in group_by]
if all(current[n][i] == last[i] for i in idx):
last = tuple(current[n][i] if i in idx \
else aggregate.get(allfields[n][i],common.generic_merge)((last[i],current[n][i])) \
for i in range(len(allfields[n]))) # merge last and current
else:
yield last
last = current[n]
else:
yield current[n]
current[n] = _t[n].next()[:N]
if group_by: yield last
if len(trackList) == 1: return trackList[0]
if fields is None:
fields = trackList[0].fields
fields = [f for f in fields if all(f in t.fields for t in trackList)]
_of = ['start','end']
if 'chr' in fields: _of = ['chr']+_of
if 'name' in fields: _of += ['name']
_of += [f for f in fields if not(f in _of)]
tl = [common.reorder(t,_of) for t in trackList]
tl = [FeatureStream(common.sentinelize(x,(sys.maxint,)*len(x.fields)),x.fields) for x in tl]
return FeatureStream(_weave(tl,len(_of)),fields=_of)
def _combine(trackList,fn,win_size,aggregate):
"""Generator - see function `combine` below."""
N = len(trackList)
fields = trackList[0].fields
trackList = [common.sentinelize(t, [sys.maxint]*len(t.fields)) for t in trackList]
init = [trackList[i].next() for i in range(N)] # the first element of each track
activity = [False]*N # a vector of boolean values for the N tracks at a given position
z = [None]*N
# If there are empty tracks, remove them, and their index from init
for i in xrange(N-1,-1,-1):
if init[i][0] == sys.maxint:
N-=1
trackList.pop(i)
init.pop(i)
if N == 0: return
available_tracks = range(N-1,-1,-1)
# Sort starts and ends of all init elements indifferently; record the origin track index.
current = [(init[i][0],i)+init[i][2:] for i in range(N)]+[(init[i][1],i) for i in range(N)]
current.sort()
# Init step: set all tracks beginning at the starting point as 'active'
is_chr = 'chr' in fields
if is_chr:
empty = (current[0][2],)+('0',)*len(fields[3:]) # write chr name if a region has no other annotation
else:
empty = ('0',)*len(fields[2:])
start = current[0][0]
while current[0][0] == start:
i = current[0][1] # track index
activity[i] = True # set this track to 'active'
z[i] = current.pop(0)[2:] # z records all meta info
k=1
while available_tracks or current:
# Load all elements within *win_size* bp in *current*
to_remove = []
limit = k * win_size
while current[0][0] >= limit:
k+=1
limit = k * win_size
for i in available_tracks:
a = [0,0]
while a[1] < limit:
a = trackList[i].next()
if a[0] == sys.maxint: # track i is completely read:
to_remove.append(i) # remove it from the tracks list
else:
current.append((a[0],i)+a[2:])
current.append((a[1],i))
for i in to_remove:
available_tracks.remove(i)
if not current: continue
current.sort()
# Calculate boolean values for this window
while current and current[0][0] < limit:
next = current[0][0]
if fn(activity):
feat_aggreg = [None]*len(fields[2:])
for n,f in enumerate(fields[2:]):
feats = tuple(zi[n] for zi in z if zi)
try: feat_aggreg[n] = aggregate.get(f,common.generic_merge)(feats)
except IndexError: feat_aggreg = empty
yield (start,next) + tuple(feat_aggreg)
while current and current[0][0] == next:
i = current[0][1] # track index
activity[i] = not(activity[i]) # reverse activity
zi = current.pop(0)[2:] # record meta info
z[i] = zi if activity[i] else None
start = next
k+=1
def test_sentinelize(self):
stream = fstream([(10,12,0.5), (14,15,1.2)], fields=['start','end','score'])
stream = sentinelize(stream,'Z')
for y in stream: x = y
self.assertEqual(x,'Z')
def _get_feature(_t, _a):
F = []
_a = common.sentinelize(_a, [sys.maxint] * len(_a.fields))
for peak in _t:
distMinBefore = distMinAfter = thresholdInter + 1
gene = dist = typeLoc = ""
geneBefore = geneAfter = strandBefore = strandAfter = None
included = 0
# keep only genes which don't start too far
for annot in _a:
F.append(annot)
if annot[0] > peak[1] + thresholdInter: break
# remove genes that end too far
fpop = -1 # always keep one gene before
for annot in F:
if annot[1] > peak[0] - thresholdInter: break
fpop += 1
if fpop > 0: F = F[fpop:]
for annot in F:
# if the peak is totally included in the gene
if (peak[0] >= annot[0]) and (annot[1] >= peak[1]):
includedGene = annot[2]
includedDist = (
annot[3]
== -1) and annot[1] - peak[1] or peak[0] - annot[0]
included = 1
# if the gene is totally included in the peak
elif (annot[0] > peak[0]) and (peak[1] > annot[1]):
includedGene = annot[2]
includedDist = 0
included = 1
else:
# if annot is not too far 3' and no intersection
if 0 < (peak[0] - annot[1]) < distMinBefore:
distMinBefore = peak[0] - annot[1]
geneBefore = annot[2]
strandBefore = annot[3]
# if intersection (annot is before)
elif annot[0] < peak[0] < annot[1]:
distMinBefore = 0
geneBefore = annot[2]
strandBefore = annot[3]
#print "gene %s overlaps begin of peak %s" % (geneBefore,peakName)
# if annot is not too far 5' and no intersection
if 0 < (annot[0] - peak[1]) < distMinAfter:
distMinAfter = annot[0] - peak[1]
geneAfter = annot[2]
strandAfter = annot[3]
# if intersection (annot is after)
elif annot[0] < peak[1] < annot[1]:
distMinAfter = 0
geneAfter = annot[2]
strandAfter = annot[3]
#print "gene %s overlaps end of peak %s" % (geneAfter,peakName)
# detect intergenic peak
if not (
included
) and distMinBefore > thresholdInter and distMinAfter > thresholdInter:
yield peak + ('', 'Intergenic', thresholdInter)
continue
# detect peak before the first or after the last gene on the chromosome
if geneBefore == None:
if distMinAfter <= thresholdInter:
gene = geneAfter
dist = distMinAfter
typeLoc = (strandAfter == 1) and "Upstream" or "Downstream"
elif geneAfter == None:
if distMinBefore <= thresholdInter:
gene = geneBefore
dist = distMinBefore
typeLoc = (strandBefore
== -1) and "Upstream" or "Downstream"
# detect peak between two genes on the same strand
elif strandBefore == strandAfter:
if strandBefore == 1:
if thresholdUTR * distMinAfter > 100 * distMinBefore:
gene = geneBefore
dist = distMinBefore
if distMinAfter < thresholdPromot:
typeLoc = "3UTR"
else:
typeLoc = "Downstream"
else:
gene = geneAfter
dist = distMinAfter
if dist < thresholdPromot:
typeLoc = "Promot"
else:
typeLoc = "Upstream"
else:
if thresholdUTR * distMinBefore > 100 * distMinAfter:
gene = geneAfter
dist = distMinAfter
if distMinBefore < thresholdPromot:
typeLoc = "3UTR"
else:
typeLoc = "Downstream"
else:
gene = geneBefore
dist = distMinBefore
if dist < thresholdPromot:
typeLoc = "Promot"
else:
typeLoc = "Upstream"
# detect peak between two genes on different strands
else:
# detect peak between 2 promoters
if strandBefore == -1:
typeLoc = "Upstream"
if distMinBefore < distMinAfter:
gene = geneBefore
dist = distMinBefore
if dist < thresholdPromot:
typeLoc = "Promot"
if distMinAfter < thresholdPromot:
typeLoc += "_Promot"
gene += "_" + geneAfter
dist = str(dist) + "_" + str(distMinAfter)
else:
gene = geneAfter
dist = distMinAfter
if dist < thresholdPromot:
typeLoc = "Promot"
if distMinBefore < thresholdPromot:
typeLoc += "_Promot"
gene += "_" + geneBefore
dist = str(dist) + "_" + str(distMinBefore)
# detect peak between 2 3UTR
else:
typeLoc = "Downstream"
# detect peak overlapping the 2 3UTR
if distMinBefore == distMinAfter:
if thresholdUTR * thresholdPromot > 100 * distMinBefore:
typeLoc = "3UTR"
typeLoc += "_" + typeLoc
gene = geneBefore + "_" + geneAfter
dist = str(distMinBefore) + "_" + str(distMinAfter)
elif distMinBefore < distMinAfter:
dist = distMinBefore
gene = geneBefore
if thresholdUTR * thresholdPromot > 100 * dist:
typeLoc = "3UTR"
else:
dist = distMinAfter
gene = geneAfter
if thresholdUTR * thresholdPromot > 100 * dist:
typeLoc = "3UTR"
if included == 1:
gene += "_" + includedGene if gene else includedGene
dist = str(dist)
dist = dist + "_" + str(includedDist) if dist else str(
includedDist)
typeLoc += "_Included" if typeLoc else "Included"
yield peak + (gene, typeLoc, dist)
def _get_feature(_t,_a):
F = []
_a = common.sentinelize(_a, [sys.maxint]*len(_a.fields))
for peak in _t:
distMinBefore = distMinAfter = thresholdInter+1
gene = dist = typeLoc = ""
geneBefore = geneAfter = strandBefore = strandAfter = None
included = 0
# keep only genes which don't start too far
for annot in _a:
F.append(annot)
if annot[0] > peak[1]+thresholdInter: break
# remove genes that end too far
fpop = -1 # always keep one gene before
for annot in F:
if annot[1] > peak[0]-thresholdInter: break
fpop += 1
if fpop>0: F = F[fpop:]
for annot in F:
# if the peak is totally included in the gene
if (peak[0]>=annot[0]) and (annot[1]>=peak[1]):
includedGene = annot[2]
includedDist = (annot[3] == -1) and annot[1]-peak[1] or peak[0]-annot[0]
included = 1
# if the gene is totally included in the peak
elif (annot[0]>peak[0]) and (peak[1]>annot[1]):
includedGene = annot[2]
includedDist = 0
included = 1
else:
# if annot is not too far 3' and no intersection
if 0 < (peak[0]-annot[1]) < distMinBefore:
distMinBefore = peak[0]-annot[1]
geneBefore = annot[2]
strandBefore = annot[3]
# if intersection (annot is before)
elif annot[0] < peak[0] < annot[1]:
distMinBefore = 0
geneBefore = annot[2]
strandBefore = annot[3]
#print "gene %s overlaps begin of peak %s" % (geneBefore,peakName)
# if annot is not too far 5' and no intersection
if 0 < (annot[0]-peak[1]) < distMinAfter:
distMinAfter = annot[0]-peak[1]
geneAfter = annot[2]
strandAfter = annot[3]
# if intersection (annot is after)
elif annot[0] < peak[1] < annot[1]:
distMinAfter = 0
geneAfter = annot[2]
strandAfter = annot[3]
#print "gene %s overlaps end of peak %s" % (geneAfter,peakName)
# detect intergenic peak
if not(included) and distMinBefore > thresholdInter and distMinAfter > thresholdInter:
yield peak+('','Intergenic',thresholdInter)
continue
# detect peak before the first or after the last gene on the chromosome
if geneBefore == None:
if distMinAfter <= thresholdInter:
gene = geneAfter
dist = distMinAfter
typeLoc = (strandAfter == 1) and "Upstream" or "Downstream"
elif geneAfter == None:
if distMinBefore <= thresholdInter:
gene = geneBefore
dist = distMinBefore
typeLoc = (strandBefore == -1) and "Upstream" or "Downstream"
# detect peak between two genes on the same strand
elif strandBefore == strandAfter:
if strandBefore == 1:
if thresholdUTR*distMinAfter > 100*distMinBefore:
gene = geneBefore
dist = distMinBefore
if distMinAfter < thresholdPromot:
typeLoc = "3UTR"
else:
typeLoc = "Downstream"
else:
gene = geneAfter
dist = distMinAfter
if dist < thresholdPromot:
typeLoc = "Promot"
else:
typeLoc = "Upstream"
else:
if thresholdUTR*distMinBefore > 100*distMinAfter:
gene = geneAfter
dist = distMinAfter
if distMinBefore < thresholdPromot:
typeLoc = "3UTR"
else:
typeLoc = "Downstream"
else:
gene = geneBefore
dist = distMinBefore
if dist < thresholdPromot:
typeLoc = "Promot"
else:
typeLoc = "Upstream"
# detect peak between two genes on different strands
else:
# detect peak between 2 promoters
if strandBefore == -1:
typeLoc = "Upstream"
if distMinBefore < distMinAfter:
gene = geneBefore
dist = distMinBefore
if dist < thresholdPromot:
typeLoc = "Promot"
if distMinAfter < thresholdPromot:
typeLoc += "_Promot"
gene += "_"+geneAfter
dist = str(dist)+"_"+str(distMinAfter)
else:
gene = geneAfter
dist = distMinAfter
if dist < thresholdPromot:
typeLoc = "Promot"
if distMinBefore < thresholdPromot:
typeLoc += "_Promot"
gene += "_"+geneBefore
dist = str(dist)+"_"+str(distMinBefore)
# detect peak between 2 3UTR
else:
typeLoc = "Downstream"
# detect peak overlapping the 2 3UTR
if distMinBefore == distMinAfter:
if thresholdUTR*thresholdPromot > 100*distMinBefore:
typeLoc = "3UTR"
typeLoc += "_"+typeLoc
gene = geneBefore+"_"+geneAfter
dist = str(distMinBefore)+"_"+str(distMinAfter)
elif distMinBefore < distMinAfter:
dist = distMinBefore
gene = geneBefore
if thresholdUTR*thresholdPromot > 100*dist:
typeLoc = "3UTR"
else:
dist = distMinAfter
gene = geneAfter
if thresholdUTR*thresholdPromot > 100*dist:
typeLoc = "3UTR"
if included == 1:
gene += "_"+includedGene if gene else includedGene
dist = str(dist)
dist = dist+"_"+str(includedDist) if dist else str(includedDist)
typeLoc += "_Included" if typeLoc else "Included"
yield peak+(gene,typeLoc,dist)
